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@dtauer
dtauer / Python-for-Professional-Developers.md
Created April 29, 2026 21:18
Python for Professional Developers Workshop

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@rohitg00
rohitg00 / llm-wiki.md
Last active May 7, 2026 14:09 — forked from karpathy/llm-wiki.md
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.
@karpathy
"""
import os # os.path.exists
import math # math.log, math.exp
@mattifestation
mattifestation / SysmonEventGUIDParser.ps1
Last active May 7, 2026 14:03
Extracts fields from sysmon process and logon GUIDs
# Author: Matthew Graeber (@mattifestation)
$Epoch = Get-Date '01/01/1970'
# Conversion trick taken from https://blogs.technet.microsoft.com/heyscriptingguy/2017/02/01/powertip-convert-from-utc-to-my-local-time-zone/
$StrCurrentTimeZone = (Get-WmiObject Win32_timezone).StandardName
$TZ = [TimeZoneInfo]::FindSystemTimeZoneById($StrCurrentTimeZone)
# Parse out all the LogonGUID fields for sysmon ProcessCreate events
Get-WinEvent -FilterHashtable @{ LogName = 'Microsoft-Windows-Sysmon/Operational'; Id = 1 } | ForEach-Object {
@ongkiii
ongkiii / IPA-Sources.md
Last active May 7, 2026 13:58
REPOS/TELEGRAM CHANNELS LIST BY u/angkitbharadwaj
@natanaelhx
natanaelhx / baremetal-setup.sh
Created May 7, 2026 13:54 — forked from mrdornellesf/baremetal-setup.sh
Bare Metal Setup - OpenClaw v3.1 (usuario dedicado + 14 camadas de seguranca)
#!/bin/bash
# =====================================================================
# Bare Metal Setup Script — OpenClaw Edition v3.1.2
# =====================================================================
# Configura uma VPS Ubuntu 24.04 com seguranca enterprise e instala
# o OpenClaw bare metal (sem Docker) em usuario dedicado.
#
# Uso:
# curl -fsSL URL -o /tmp/setup.sh && chmod +x /tmp/setup.sh
# /tmp/setup.sh